Abstract: Abstract: Pneumonia is a common multi-infectious disease that predisposes the elderly and those with weakened immune systems to infection， and early detection can help with later treatment. Factors such as the location， density and clarity of lung lesions can affect the accuracy of pneumonia image classification. With the development of deep learning， convolutional neural network is widely used in medical image classification tasks， however， the learning ability of the network depends on the number of training samples and labels. Aiming at the classification of pneumonia images in computed tomography （CT）， a network model based on self-supervised comparative learning （MCLSE） is proposed， which can learn features from unmarked data and improve the accuracy of the network model. Firsly， auxiliary tasks were designed to mine representations from unmarked images to complete pre-training， improving the ability of the model to learn data mapping relationships in vector space. Secondly， the convolutional neural network is used to extract features. In order to effectively capture higher level feature information， the compression excitation network is selected to improve the classification model and the correlation between the feature channels is modeled. Finally， the trained weights are loaded into the improved classification model， and the network is trained again with marked data in the downstream task. Experiments were carried out on open data sets， SARS-CoV-2 CT and CT Scan for COVID-19 Classification. The results show that the accuracy of the MCLSE model in this paper for the overall sample classification reached 99.19% and 99.75%， respectively， which was greatly improved compared with the mainstream model.

Key words: Key words: COVID-19 image, medical image classification, convolutional neural network, self-supervised learning, contrastive learning